Shape similarity retrieval under affine transforms

Abstract

The maxima of curvature scale space (CSS) image have already been used to represent 2-D shapes in different applications. The representation has shown robustness under the similarity transformations. Scaling, orientation changes, translation and even noise can be easily handled by the representation and its associated matching algorithm. In this paper, we examine the robustness of the representation under general affine transforms. We have a database of 1100 images of marine creatures. The contours in this database demonstrate a great range of shape variation. A database of 5000 contours has been constructed using 500 real object boundaries and 4500 contours which are the affine transformed versions of real objects. The CSS representation is then used to find similar shapes from this prototype database. The results provide substantial evidence of stability of the CSS image and its contour maxima under affine transformation. The method is also evaluated objectively through a large classified database and its performance is compared with the performance of two well-known methods, namely Fourier descriptors and moment invariants. The CSS shape descriptor has been selected for MPEG-7 standardization.